Industrial internet of things, control methods, and storage medium for sheet workpiece processing

ABSTRACT

The present disclosure provides an Industrial Internet of Things, a control method and storage medium for sheet workpiece processing. The Industrial Internet of Things includes a detecting module and a processing module; the detecting module is configured to detect a machining process of a sheet workpiece to obtain detection information; and the processing module is configured to adjust a production device based on the detection information. By detecting and adjusting a sheet workpiece, on the one hand, the present disclosure can effectively improve the subsequent processing accuracy of the sheet workpiece, on the other hand, the present disclosure can simplify an adjustment process and improve the adjustment efficiency of a bearing mechanism of a target sheet workpiece.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure claims priority of Chinese Patent Application No.202310044323.X, filed on Jan. 30, 2023, the entire contents of which arehereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to intelligent manufacturing IndustrialInternet of Things technology, and in particular, to an IndustrialInternet of Things, a control method and storage medium for sheetworkpiece processing.

BACKGROUND

A sheet workpiece is a common device to be processed in industrialmanufacturing, such as LED panels, wafers, friction sheets, and flyingsheets, which often requires high processing accuracy. In order toimprove the processing accuracy of the sheet workpiece, it is necessaryto improve the recognition accuracy of the sheet workpiece beforeprocessing. However, due to the relatively large area and relativelysmall thickness of the sheet workpiece, rotation and translation areprone to occur during a grasping process, and deformation is easilycaused by the offset of a processing device during a machining process,so error is prone to occur.

Therefore, it is hoped that an Industrial Internet of Things, a controlmethod and storage medium for processing a sheet workpiece can beprovided, so as to reduce error during sheet workpiece processing.

SUMMARY

One or more embodiments of the present disclosure provide an IndustrialInternet of Things, a control method and storage medium for sheetworkpiece processing. The Industrial Internet of Things includes: adetecting module and a processing module. The detecting module isconfigured to detect a machining process of a sheet workpiece to obtaindetection information; and the processing module is configured to adjusta production device based on the detection information.

First, the embodiments of the present disclosure provide an IndustrialInternet of Things for sheet workpiece processing, comprising a userplatform, a service platform, a management platform, a sensor networkplatform, and an object platform connected in sequence, the detectingmodule executed by the object platform and the sensor network platform,the processing module executed by the service platform, the managementplatform, and the user platform, wherein the management platformincludes: an obtaining unit configured to obtain a first target image ona production line through the sensor network platform; wherein the firsttarget image is an image of a target sheet workpiece under a fixed focallength condition; a generating unit configured to perform sharpnessanalysis on the first target image to generate a sharpness matrix,calculate a tilt direction and tilt angle of the target sheet workpieceas tilt data according to the sharpness matrix; and determinedeformation data of the target sheet workpiece through a first presetmethod based on the first target image; a correcting unit configured toinput the tilt data and the first target image into a tilt correctionmodel, and receive a second target image output by the tilt correctionmodel; wherein the second target image is an image of the target sheetworkpiece in a completely horizontal state; a calibrating unit includinga first calibrating unit and a second calibrating unit; wherein thefirst calibrating unit is configured to perform calibration andidentification on the second target image, generate horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, and calculate tilt adjustment data according to thetilt data; the second calibrating unit is configured to adjust clampingparameters through a second preset method based on tilt data anddeformation data of a plurality of target sheet workpieces; an adjustingunit configured to send the horizontal adjustment data, the rotationaladjustment data, and the tilt adjustment data to the production linethrough the sensor network platform to adjust a bearing mechanism of thetarget sheet workpiece; a communicating unit configured to send thehorizontal adjustment data, the rotational adjustment data, and the tiltadjustment data to the user platform through the service platform fordisplay.

In the prior art, a sheet workpiece is generally grasped by a suctioncup manipulator and placed on a processing platform for processing, andthe processing platform often uses a suction cup to absorb and fix thesheet workpiece, which makes the sheet workpiece prone to tilt and shiftif the suction cup control is not accurate enough, and requiresalignment of the sheet workpiece.

The embodiments of the present disclosure adopt a scheme that uses acamera with a fixed focal length to capture the sheet workpiece foralignment of the sheet workpiece when being applied. Different from theautomatic focusing of the machine vision camera in the prior art, thefirst target image in the embodiments of the present disclosure iscaptured by a camera with a fixed focal length on a sheet workpiece.Since the focal length is fixed, when the sheet workpiece is not flatenough, there is a difference in the imaging effect of different regionsof the sheet workpiece, and the difference can be expected. Performingsharpness analysis on the first target image to generate the sharpnessmatrix can be performed in various ways in the prior art, such as the10/90 rising distance approach, and the frequency domain approach. Dueto the particularity of the sharpness calculation, the sharpness matrixis not a pixel matrix, instead, the first target image is divided intoseveral regular regions and the sharpness is calculated for each region.A tilt direction and a tilt angle of the target sheet workpiece may becalculated through the sharpness matrix.

In the embodiments of the present disclosure, in order to reduce a countof times of the camera taking pictures, the first target image iscorrected through the tilt data to an image of the target sheetworkpiece in a completely horizontal state. The correction process ismainly realized through a tilt correction model, and the tilt correctionmodel may include a decision-making model such as a neural network modelthat can be used for decision-making. The decision-making modelgenerates a decision-making plan for image processing according to thetilt data, and then an image processing model processes the first targetimage.

In the embodiments of the present disclosure, since a tilt correction iscompleted for the second target image, performing the horizontaladjustment and rotational adjustment of the target sheet workpiece bythe second target image may be more accurate. When sending thehorizontal adjustment data, rotational adjustment data, and tiltadjustment data to a corresponding bearing mechanism for adjustment, thetilt angle should be adjusted first, then the rotation angle isadjusted, and finally the horizontal angle is adjusted to ensure theaccuracy of the adjustment.

In the embodiments of the present disclosure, by capturing one shot ofthe target sheet workpiece, tilt adjustment, horizontal adjustment androtational adjustment of the target sheet workpiece can be performed. Onthe one hand, the subsequent processing accuracy of the sheet workpieceis effectively improved, and on the other hand, the adjustment can besimplified and the adjustment efficiency of a bearing mechanism of thetarget sheet workpiece can be improved.

In a possible implementation manner, the generating unit is furtherconfigured to search for sharpness elements with a sharpness lower thana standard value and with a same sharpness in the sharpness matrix assame degree sharpness elements; connecting the same degree sharpnesselements into a straight line in the first target image, and calculatingthe tilt direction according to the straight line.

In a possible implementation manner, the generating unit is furtherconfigured to obtain a plurality of straight lines connected by aplurality of sets of same degree sharpness elements in the first targetimage, and calculate the tilt angle and tilt direction according toposition relationships between the plurality of straight lines and thetilt angle.

In a possible implementation manner, the correction unit is furtherconfigured to: when inputting the tilt data and the first target imageinto a tilt correction model, determine a correction direction accordingto the tilt direction, determine a correction gradient according to thetilt angle through the tilt correction model; and performing stretchingprocessing corresponding to the correction gradient the first targetimage to the second target image according to the correction directionthrough the tilt correction model.

In a possible implementation manner, the calibrating unit is furtherconfigured to: identify a first feature point and a second feature pointin the second target image; wherein the first feature point correspondsto an identification point or an identification area of the target sheetworkpiece; the second feature point corresponds to anotheridentification point or another identification area of the target sheetworkpiece; establish a connection line between the first feature pointand the second feature point as a calibration connection line; calculatean angle between the calibration connection line and a standard line ina standard template as the rotational adjustment data; wherein thestandard template is an image of the target sheet workpiece in astandard processing position; and the standard connection line is aconnection line between the first feature point and the second featurepoint in the standard template; calculate a difference between amidpoint of the calibration connection line and a midpoint of thestandard connection line on a horizontal axis and vertical axis in astandard horizontal coordinate system as the horizontal adjustment data;wherein the standard horizontal coordinate system is a Cartesiancoordinate system established on a horizontal plane in the standardtemplate.

In a possible implementation manner, the service platform includes ageneral service platform and at least two service sub-platforms, anddifferent service sub-platforms are configured to receive differenttypes of data transmitted by the management platform; the generalservice platform summarizes data collected by all the servicesub-platforms and sends the data to the user platform for display. Themanagement platform includes a plurality of mutually independentmanagement sub-platforms, and an obtaining unit, generating unit,correcting unit, calibrating unit, adjusting unit and communicating unitare configured in a same management sub-platform. The sensor networkplatform includes a general sensor network platform and at least twosensor network sub-platforms, the general sensor network platformreceives all production data on the production line, and the sensornetwork sub-platforms send different types of production data to themanagement platform respectively.

Second, the embodiments of the present disclosure provide a controlmethod of Industrial Internet of Things for sheet workpiece processing,wherein the control method includes: obtaining a first target image on aproduction line through a sensor network platform; wherein the firsttarget image is an image of a target sheet workpiece under a fixed focallength condition; performing sharpness analysis on the first targetimage to generate a sharpness matrix, and calculating a tilt directionand a tilt angle of the target sheet workpiece as tilt data according tothe sharpness matrix; inputting the tilt data and the first target imageinto a tilt correction model, and receiving a second target image outputby the tilt correction model; wherein the second target image is animage of the target sheet workpiece in a completely horizontal state;performing calibration and identification on the second target image,generating horizontal adjustment data and rotational adjustment datacorresponding to the second target image, and calculating tiltadjustment data according to the tilt data; sending the horizontaladjustment data, rotational adjustment data and tilt adjustment data tothe production line through the sensor network platform to adjust abearing mechanism of the target sheet workpiece; and sending thehorizontal adjustment data, rotational adjustment data, and tiltadjustment data to a user platform through a service platform fordisplay.

In a possible implementation manner, calculating a tilt direction and atilt angle of the target sheet workpiece as tilt data according to thesharpness matrix includes: searching for sharpness elements with asharpness lower than a standard value and with a same sharpness in thesharpness matrix as same degree sharpness elements; connecting the samedegree sharpness elements into a straight line in the first targetimage, and calculating the tilt direction according to the straightline; obtaining a plurality of straight lines connected by a pluralityof sets of same degree sharpness elements in the first target image, andcalculate the tilt angle according to position relationships between theplurality of straight lines and the tilt direction.

In a possible implementation manner, inputting the tilt data and thefirst target image into a tilt correction model, and receiving a secondtarget image output by the tilt correction model includes: wheninputting the tilt data and the first target image into the tiltcorrection model, determining, through the tilt correction model, acorrection direction according to the tilt direction, determining,through the tilt correction model, a correction gradient according tothe tilt angle; and performing, through the tilt correction model,stretching processing corresponding to the correction gradient on thefirst target image to the second target image according to thecorrection direction.

In a possible implementation manner, performing calibration andidentification on the second target image, and generating horizontaladjustment data and rotational adjustment data corresponding to thesecond target image includes: identifying a first feature point and thesecond feature point in the second target image; wherein the firstfeature point corresponds to an identification point or anidentification area of the target sheet workpiece; and the secondfeature point corresponds to another identification point or anotheridentification area of the target sheet workpiece; establishing aconnection line between the first feature point and the second featurepoint as a calibration connection line; calculating an angle between thecalibration connection line and a standard connection line in a standardtemplate as the rotational adjustment data; wherein the standardtemplate is an image of the target sheet workpiece in a standardprocessing position; and the standard connection line is a connectionline between the first feature point and the second feature point in thestandard template; calculating a difference between a midpoint of thecalibration connection line and a midpoint of the standard connectionline on a horizontal axis and vertical axis in a standard horizontalcoordinate system as the horizontal adjustment data; wherein thestandard horizontal coordinate system is a Cartesian coordinate systemestablished on a horizontal plane in the standard template.

Third, the embodiments of the present disclosure provide anon-transitory computer-readable storage medium storing computerinstructions, wherein a computer implements the control method ofIndustrial Internet of Things for sheet workpiece processing whenreading the computer instructions.

In some embodiments of the present disclosure, the Industrial Internetof Things, control method, and storage medium for sheet workpieceprocessing can complete tilt adjustment, horizontal adjustment, androtational adjustment of the target sheet workpiece by capturing atarget sheet workpiece. On the one hand, it effectively improves thesubsequent processing accuracy of the sheet workpiece, on the otherhand, it can simplify an adjustment process, and improve the adjustmentefficiency of a bearing mechanism of the target sheet workpiece.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are not limited, in theseembodiments, the same numeral denotes the same structure, wherein:

FIG. 1 is a schematic diagram illustrating an Industrial Internet ofThings for sheet workpiece processing according to some embodiments ofthe present disclosure;

FIG. 2 is a schematic diagram illustrating an exemplary structure of theIndustrial Internet of Things for sheet workpiece processing accordingto some embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating a control method of IndustrialInternet of Things for sheet workpiece processing according to someembodiments of the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process for determiningdeformation data according to some embodiments of the presentdisclosure;

FIG. 5 a is a schematic diagram illustrating an exemplary relativeposition model according to some embodiments of the present disclosure;

FIG. 5 b is a schematic diagram illustrating an exemplary relativeposition diagram according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for adjustingclamping parameters according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to illustrate the technical solutions of the embodiments of thepresent disclosure more clearly, the following briefly introduces thedrawings that need to be used in the description of the embodiments.Apparently, the accompanying drawings in the following description areonly some examples or embodiments of the present disclosure, and thoseskilled in the art can also apply the present disclosure to othersimilar scenarios. Unless obviously obtained from the context or thecontext illustrates otherwise, the same numeral in the drawings denotesthe same structure or operation.

It should be understood that the words “system”, “device”, “unit” and/or“module” as used herein is a method for distinguishing differentcomponents, elements, parts, parts, or assemblies of different levels.However, the words may be replaced by other expressions if other wordscan achieve the same purpose.

As indicated in the present disclosure and claims, the terms “a”, “an”,and/or “the” are not specific to the singular and may include the pluralunless the context clearly indicates an exception. Generally speaking,the terms “comprise” and “include” only suggest the inclusion of clearlyidentified steps and elements, and these steps and elements do notconstitute an exclusive list, and the method or device may also containother steps or elements.

The flowchart is used in the present disclosure to illustrate theoperations performed by the system according to the embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations are not necessarily performed in exact order.Instead, various steps may be processed in reverse order orsimultaneously. At the same time, other operations can be added to theseprocedures, or a certain step or steps can be removed from theseprocedures.

FIG. 1 is a schematic diagram illustrating an Industrial Internet ofThings for sheet workpiece processing according to some embodiments ofthe present disclosure.

In some embodiments, Industrial Internet of Things 100 for sheetworkpiece processing may include detecting module 110 and processingmodule 120.

The detecting module 110 may be configured to detect a machining processof a sheet workpiece to obtain detection information. For more detailsabout the detection information, please refer to FIG. 3 and its relateddescriptions.

The processing module 120 may be configured to adjust a productiondevice based on the detection information. For more details aboutadjusting the production device, please refer to FIG. 3 , FIG. 4 , FIG.5 , and FIG. 6 and their related descriptions.

In some embodiments, the detecting module 110 may be executed by anobject platform or a sensor network platform. The processing module 120may be executed by a service platform, a management platform, or a userplatform. For more details about the service platform, managementplatform, sensor network platform, and object platform, please refer toFIG. 2 and its related descriptions.

It should be understood that the system and modules shown in FIG. 1 maybe implemented in various ways. It should be noted that the abovedescription of the Industrial Internet of Things and modules for sheetworkpiece processing is only for the convenience of description, anddoes not limit the present disclosure to the scope of the embodiments.It should be understood that for those skilled in the art, afterunderstanding the principle of the system, it is possible to combinevarious modules or form a sub-system to connect with other moduleswithout departing from the principle arbitrarily. In some embodiments,the detecting module 110 and the processing module 120 disclosed in FIG.1 may be different modules in one system, or one module realizing thefunctions of the above-mentioned two or more modules. For example, eachmodule may share one storage module, or each module may have its ownstorage module. Such deformations are within the protection scope of thepresent disclosure.

FIG. 2 is a schematic diagram illustrating an exemplary structure of theIndustrial Internet of Things for sheet workpiece processing accordingto some embodiments of the present disclosure.

As a functional system shown in FIG. 2 , the Industrial Internet ofThings for sheet workpiece processing includes a user platform, aservice platform, a management platform, a sensor network platform, andan object platform connected in sequence. The object platform isconfigured as a camera device, such as a camera with a fixed focallength. The management platform includes an obtaining unit configured toobtain a first target image on a production line through the sensornetwork platform; the first target image is an image of a target sheetworkpiece under a fixed focal length condition; a generating unitconfigured to perform sharpness analysis on the first target image togenerate a sharpness matrix and calculate a tilt direction and a tiltangle of the target sheet workpiece as tilt data according to thesharpness matrix; a correcting unit configured to input the tilt dataand the first target image into a tilt correction model, and receive asecond target image output by the tilt correction model; the secondtarget image is an image of the target sheet workpiece in a completelyhorizontal state; a calibrating unit configured to perform calibrationand identification on the second target image, generate horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, and calculate tilt adjustment data according to thetilt data; an adjusting unit configured to send the horizontaladjustment data, the rotational adjustment data and the tilt adjustmentdata to the production line through the sensor network platform toadjust a bearing mechanism of the target sheet workpiece; and acommunicating unit configured to send the horizontal adjustment data,the rotational adjustment data, and the tilt adjustment data to the userplatform through the service platform for display.

In a possible implementation manner, the service platform includes ageneral service platform and at least two service sub-platforms,different service sub-platforms are configured to receive differenttypes of data transmitted by the management platform; the generalservice platform summarizes data collected by all the servicesub-platforms and sends the data to the user platform for display. Themanagement platform includes a plurality of mutually independentmanagement sub-platforms, and an obtaining unit, generating unit,correcting unit, calibrating unit, adjusting unit and communicating unitare configured in a same management sub-platform. The sensor networkplatform includes a general sensor network platform and at least twosensor network sub-platforms, and the general sensor network platformreceives all production data on the production line, and sends differenttypes of production data to the management platform respectively.

In some embodiments, a plurality of service sub-platforms respectivelyobtains one of the horizontal adjustment data, rotational adjustmentdata, and tilt adjustment data from the management platform, which aresummarized by a general service platform and sent to a user platform fordisplay. The general sensor network platform obtains first target imagesof all target sheet workpieces on a production line, and a plurality ofsensor network sub-platforms obtain the first target images of differenttarget sheet workpieces from the general sensor network platform, andsend the first target images to the management platform respectively.

As a physical system shown in FIG. 2 , the Industrial Internet of Thingsfor sheet workpiece processing includes a terminal device, a firstserver, a second server, a plurality of gateway servers, and a pluralityof devices. The first server includes a general server and a pluralityof sub-servers. The second server includes a plurality of sub-servers.In some embodiments, the terminal device performs data interaction withthe general server in the first server; the general server in the firstserver performs data interaction with the plurality of sub-servers; thefirst server and the second server perform data interaction through arespective plurality of sub-servers; the plurality of sub-servers of thesecond server perform data interaction with a plurality of gatewayservers; the plurality of gateway servers interact with a generalgateway server, and the general gateway server performs data interactionwith the plurality of devices.

FIG. 3 is a flowchart illustrating a control method of IndustrialInternet of Things for sheet workpiece processing according to someembodiments of the present disclosure. As shown in FIG. 3 , process 300includes the following steps. In some embodiments, the process 300 maybe executed by a management platform.

In step 310, obtaining, by an obtaining unit, a first target image on aproduction line through a sensor network platform; the first targetimage being an image of a target sheet workpiece under a fixed focallength condition.

For example, the first target image may include an image of the targetsheet workpiece captured by a fixed camera with a focal length of 30 cm,40 cm, or the like.

When the embodiment of the present disclosure is implemented, analignment scheme of a sheet workpiece is completed by using a camerawith a fixed focal length to capture the sheet workpiece. Different fromthe automatic focusing of the machine vision camera in the prior art,the first target image in the embodiment of the present disclosure isobtained through capturing the sheet workpiece with a camera with afixed focal length. Since the focal length is fixed, when the sheetworkpiece is not flat enough, there is a difference in the imagingeffect of different regions of the sheet workpiece, and the differencemay be expected.

In step 320, performing, by a generating unit, sharpness analysis on thefirst target image to generate a sharpness matrix, and calculating atilt direction and a tilt angle of the target sheet workpiece as tiltdata according to the sharpness matrix; and determining deformation dataof the target sheet workpiece through a first preset method based on thefirst target image.

The sharpness matrix may be a matrix used to reflect the definition ofthe image plane and the sharpness of the image edge. For example, thesharpness matrix may include a 4×5 matrix, a 9×9 matrix, or the like.

In some embodiments, the generating unit may generate a first targetimage sharpness matrix divided into a plurality of regular regions byperforming sharpness analysis on the first target image. For example,the management platform may divide the first target image into a regular9×9 sharpness matrix through the sharpness analysis.

The tilt data may be data reflecting a tilt degree of the target sheetworkpiece. For example, the tilt data may include a tilt direction and atilt angle of the target sheet workpiece, etc.

In some embodiments, the management platform may obtain the tiltdirection and the tilt angle of the target sheet workpiece as tilt databy calculating the sharpness matrix.

The sharpness analysis on the first target image to generate thesharpness matrix may be performed in various ways in the prior art, suchas the 10/90 rising distance approach and frequency domain approach. Dueto the particularity of the sharpness calculation, the sharpness matrixis not a pixel matrix, instead, the sharpness calculation is performedon each area after dividing the first target image into a plurality ofregular areas. The tilt direction and the tilt angle of the target sheetworkpiece may be calculated through the sharpness matrix.

In a possible implementation manner, calculating the tilt direction andtilt angle of the target sheet workpiece as tilt data according to thesharpness matrix includes: searching for sharpness elements with asharpness lower than a standard value and with a same sharpness in thesharpness matrix as same degree sharpness elements; connecting the samedegree sharpness elements into a straight line in the first targetimage, and calculating the tilt direction according to the straightline; obtaining a plurality of straight lines connected in the firsttarget image by a plurality of sets of same degree sharpness elements,and calculating the tilt angle according to position relationshipsbetween the plurality of straight lines and the tilt direction.

The same degree sharpness elements may be elements with a sharpnesslower than a standard value and with a same sharpness, and the standardvalue may be set by an expert. For example, if the standard value is 50,elements with a sharpness value lower than 50 in a sharpness matrix areextracted, and elements with a same sharpness among the extractedelements are taken as same degree sharpness elements.

In some embodiments, the management platform may connect the same degreesharpness elements into a straight line in the first target image, andcalculate the tilt direction according to the straight line. Forexample, the tilt direction may be a linear direction, or the like.

In some embodiments, the management platform may obtain a plurality ofstraight lines connected by a plurality of sets of same degree sharpnesselements in the first target image, and calculate the tilt angleaccording to position relationships and tilt direction of the pluralityof straight lines. For example, the management platform may obtain thetilt angle by calculating an average value of the position relationshipsand tilt direction of the plurality of straight lines.

When the embodiment of the present disclosure is implemented,calculating the tilt direction through the sharpness matrix may also berealized through a trained sharpness recognition model. The specificcalculation content of the sharpness recognition model includes findingsharpness elements with a same sharpness, and the sharpness elementsrefer to elements in a sharpness matrix. At the same time, the sharpnessrecognition model also needs to identify and connect same degreesharpness elements into a straight line. Since the corresponding samesharpness in a first target image may appear on both sides of a rotationaxis, it is also necessary to connect the distribution of the samedegree sharpness elements on both sides of the rotation axis into twomutually parallel lines after recognition. According to theabove-mentioned generated straight lines, the tilt direction may becalculated, which is is generally the vertical direction of thesestraight lines.

When the embodiment of the present disclosure is implemented, based onthe above embodiments, a distance between the plurality of straightlines and a resulted sharpness change gradient can be calculated,thereby calculating the tilt angle. Specifically, a sharpnessrecognition model may be used for implementation, by comparing sharpnesschange gradient with a preset gradient change table to calculate thetilt angle, in which the gradient change table is a correspondingrelationship between the tilt angle and the sharpness change gradient.The gradient change table may be a fitted table or a dot matrix table.If the gradient change table is a dot matrix table, the tilt angle needsto be calculated by interpolation.

In some embodiments of the present disclosure, the management platformdetermines the tilt angle of the target sheet workpiece by calculationbased on the same degree sharpness elements in the sharpness matrix.Determining tilt angle in this way can reduce error and improve theaccuracy of the tilt data, which is convenient for subsequentadjustments.

The deformation data may be data reflecting a change in shape andposition of the target sheet workpiece. For example, the deformationdata may include size error of the target sheet workpiece, relativeposition error of the target sheet workpiece, or the like.

The size error of the target sheet workpiece may be a difference betweena processed target sheet workpiece and a standard processed product. Forexample, an error between the size (e.g., length, width, or area) of thetarget sheet workpiece and the size of a standard processed product. Thesize of a standard processed product maybe adjusted according to anactual situation. The size error of the target sheet workpiece may bedetermined based on a relative position diagram, and the details of therelative position diagram can be found in the related descriptionsbelow.

The relative position error of the target sheet workpiece may be adifference of relative position between a processed target sheetworkpiece and a standard processed product in space. In someembodiments, the relative position error of the target sheet workpiecemay include a difference between a relative position of an actualmarking point or marking area and a standard relative position on eachtarget sheet workpiece. The relative position may be represented by agraph structure, and further details can be found in the relateddescriptions below.

The relative position error may be represented by a numerical value ofthe relative position error of the target sheet workpiece. For example,if the relative position error of the target sheet workpiece is 0.1, itmeans that the relative position error of the target sheet workpiece isrelatively small.

In some embodiments, the generating unit may determine the deformationdata of the target sheet workpiece through a first preset method basedon the first target image. For example, the generating unit may comparethe first target image with a standard image, and calculate the obtainedsize error and relative position error as the deformation data. Thestandard image may be an ideal image determined by calculation. For moredetails about the first preset method, please refer to FIG. 4 and itsrelated descriptions.

In some embodiments, the generating unit may determine the relativeposition diagram of a sheet workpiece through a relative position modelbased on the first target image; determine the relative position errorand size error of the sheet workpiece as the deformation data based onthe relative position diagram of the sheet workpiece. For more detailsabout determining the deformation data, please refer to FIG. 4 and FIG.5 and their related descriptions.

During the implementation of the embodiment of the present disclosure,in order to reduce a secondary photographing of the target sheetworkpiece, a method of correcting a first target image is adopted forsubsequent adjustment processing. The main correction method is to use atilt correction model for processing, wherein the processing method isto stretch the image according to a correction direction and correctiongradient and includes performing distortion repair on the stretchedimage.

In step 330, inputting, by a correcting unit, the tilt data and thefirst target image into a tilt correction model, and receiving a secondtarget image output by the tilt correction model; the second targetimage being an image of the target sheet workpiece in a completelyhorizontal state.

In some embodiments, when the tilt data and the first target image areinput into the tilt correction model, determining, through the tiltcorrection model, a correction direction according to the tiltdirection, determining, through the tilt correction model, a correctiongradient according to the tilt angle; and performing, through the tiltcorrection model, stretching process corresponding to the correctiongradient on the first target image to the second target image accordingto the correction direction.

In some embodiments, the tilt correction model may be used to correct atilt level of an image so that the image content is completelyhorizontal. There may be various types of tilt correction models,including a neural network model, a graph neural network model, or thelike.

In some embodiments, input data of the tilt correction model may includethe tilt data of the target sheet workpiece and the first target image;output data of the tilt correction model may include the second targetimage.

The second target image may be an image of the target sheet workpiece ina completely horizontal state.

In some embodiments, the tilt correction model may determine acorrection direction based on the tilt direction, and a correctiongradient based on the tilt angle. For example, if the tilt direction andtilt angle of the target sheet workpiece are clockwise deflection of 4°,a corresponding correction direction and correction gradient may becounterclockwise rotation of 4°.

In some embodiments, the tilt correction model may perform stretchingprocessing corresponding to the correction gradient on the first targetimage based on the correction direction to determine the second targetimage. For example, if the first target image shows that the tiltdirection and tilt angle of the target sheet workpiece are clockwisedeflection of 5°, the tilt correction model may determine the secondtarget image by performing stretching processing on the first targetimage with the correction gradient in the clockwise 5° direction.

In some embodiments of the present disclosure, the tilt correction modelmay determine the second target image by obtaining the correctiondirection and correction gradient of the first target image, which canreduce an error of the second target image, so that the target sheetworkpiece in the obtained second target image tends to be more perfectlylevel.

In some embodiments, the tilt correction model may be obtained bytraining a plurality of labeled first training samples. For example, theplurality of labeled first training samples may be input into an initialtilt correction model, a loss function is constructed through a firstlabel and a result of the initial tilt correction model, and parametersof the initial tilt correction model may be updated iteratively bygradient descent or other approaches based on the loss function. When apreset condition is met, a model training is completed, and a trainedtilt correction model is obtained. The preset condition may be that theloss function converges, a count of iterations reaches a threshold, orthe like.

In some embodiments, the first training samples may at least includetilt data of a sample sheet workpiece and a corresponding first targetimage. The first label may be a second target image corresponding to thesample sheet workpiece. A label may be obtained based on historical dataor manually labeled.

In some embodiments of the present disclosure, the corrected secondtarget image is obtained by inputting the tilt data of the target sheetworkpiece and the first target image into the tilt correction model.Through this correction approach, the obtained second target image ismore accurate, which reduces error and improves correction efficiency.

In step 340, performing, by a calibrating unit, calibration andidentification on the second target image, generating horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, calculating tilt adjustment data based on the tiltdata, and adjusting clamping parameters based on tilt data anddeformation data of a plurality of target sheet workpieces through asecond preset method.

The calibrating unit may include a first calibrating unit and a secondcalibrating unit. The first calibrating unit may perform calibration andidentification on the second target image, generate the horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, and calculate the tilt adjustment data according tothe tilt data. The second calibrating unit may adjust the clampingparameters through the second preset method based on the tilt data anddeformation data of the plurality of target sheet workpieces.

The first calibrating unit may perform calibration processing on thesecond target image.

In some embodiments, the second target image may be calibrated andidentified by the first calibrating unit to generate the horizontaladjustment data and rotational adjustment data corresponding to thesecond target image.

In some embodiments, the first calibrating unit may calculate the tiltadjustment data based on the tilt data of the target sheet workpiece.

The horizontal adjustment data may be position data for adjusting thesecond target image in the horizontal direction. For example, if anactual position coordinate of an identification area of the secondtarget image is (6, 9), and a corresponding standard position coordinateis (6, 8), then the horizontal adjustment data may be (0, -1), etc.Coordinate may be determined based on a standard horizontal coordinatesystem.

The rotational adjustment data may be orientation data for adjusting thesecond target image on a horizontal plane. For example, if deviationbetween an actual orientation of the second target image and a standardorientation is 5° counterclockwise, the rotational adjustment data maybe clockwise rotation of 5° based on a center of the target sheetworkpiece, etc.

The tilt adjustment data may be data for adjusting a stretching level ofthe second target image. For example, if the tilt data of the targetsheet workpiece is tilting 3° downward in the direction of clockwisedeflection of 1°, the calculated tilt adjustment data may be stretching3° upward in the direction of 1° clockwise, etc.

In a possible implementation manner, performing calibration andidentification on the second target image, and generating the horizontaladjustment data and rotational adjustment data corresponding to thesecond target image includes: identifying a first feature point and asecond feature point in the second target image, wherein the firstfeature point corresponds to an identification point or identificationarea of the target sheet workpiece; and the second feature pointcorresponds to another identification point or identification area ofthe target sheet workpiece; establishing a connection line between thefirst feature point and the second feature point as a calibrationconnection line; calculating an angle between the calibration connectionline and a standard connection line in a standard template as therotational adjustment data; wherein the standard template is an image ofthe target sheet workpiece in a standard processing position; and thestandard connection line is a connection line between a first featurepoint and a second feature point in the standard template; calculating adifference between a midpoint of the calibration connection line and amidpoint of the standard connection line on a horizontal axis and avertical axis in a standard horizontal coordinate system as thehorizontal adjustment data; wherein the standard horizontal coordinatesystem is a Cartesian coordinate system established in a horizontalplane in the standard template.

The first feature point may be an identification point or anidentification area corresponding to the target sheet workpiece. Thesecond feature point may be another identification point or anotheridentification area corresponding to the target sheet workpiecedifferent from the first feature point. For example, the first featurepoint and the second feature point may be respectively an identificationpoint (1, 2) and an identification point (3, 4) in the target sheetworkpiece.

In some embodiments, the management platform may determine the firstfeature point and the second feature point based on the second targetimage. For example, the management platform may determine coordinates ofan identification area corresponding to the target sheet workpiece basedon the second target image, and determine the first feature point andthe second feature point based on the coordinates.

The calibration connection line may be a connection line for calibratingthe second target image. For example, the calibration connection linemay be a straight line for obtaining adjustment parameters of the secondtarget image (the horizontal adjustment data, the rotational adjustmentdata).

In some embodiments, the management platform may determine a lineconnecting the first feature point and the second feature point as thecalibration connection line.

The standard template may be an image of the target sheet workpiece in astandard processing position.

In some embodiments, the management platform may determine the standardtemplate based on each image with the smallest adjustment parameters inhistorical processing data.

The standard connection line may be used as a reference for adjustingthe second target image. For example, the standard connection line maybe compared with the calibration connection line to obtain adjustmentparameters (the horizontal adjustment data, the rotational adjustmentdata) of the second target image.

In some embodiments, the management platform may determine a lineconnecting the first feature point and the second feature point in thestandard template as the standard connection line.

The rotational adjustment data may be parameters for adjusting theorientation of the target sheet workpiece in horizontal direction. Forexample, the rotational adjustment data may include counterclockwiserotation of 10°, or the like.

In some embodiments, the management platform may determine therotational adjustment data based on the calibration connection line andthe standard connection line. For example, the management platform maycalculate an angle between the calibration connection line and thestandard connection line in the standard template as the rotationaladjustment data.

The standard horizontal coordinate system may be a Cartesian coordinatesystem established on a horizontal plane in the standard template.

The horizontal adjustment data may be data for adjusting the position ofthe target sheet workpiece in horizontal direction. For example, thehorizontal adjustment data may include horizontal adjustment distance of(1, -1), or the like.

In some embodiments, the management platform may determine thehorizontal adjustment data based on the calibration connection line andthe standard connection line. For example, the management platformcalculates a difference between a midpoint of the calibration connectionline and a midpoint of the standard connection line on a horizontal axisand a vertical axis in a standard horizontal coordinate system as thehorizontal adjustment data.

When the embodiment of the present disclosure is implemented, a schemeof performing rotational adjustment and horizontal displacementadjustment through two feature points is adopted, wherein the featurepoints are features on the target sheet workpiece, including but notlimited to parts, screws, signs, etc., an angle that needs to be rotatedand adjusted may be calculated through the connection line between twofeature points. Since the scheme does not require very delicaterecognition of feature points, it can reduce a count of calculationsgenerated by accurate template matching. Similarly, a count of requiredhorizontal displacement adjustment may be calculated by calibrating adisplacement deviation of the midpoint of the connection line. Thehorizontal adjustment data refers to adjustment data that moveshorizontally or vertically in the standard horizontal coordinate system.

In some embodiments of the present disclosure, the management platformdetermines horizontal adjustment parameters and rotational adjustmentparameters of the target sheet workpiece based on the calibrationconnection line and the standard connection line. In this way, error ofthe horizontal adjustment parameters and the rotational adjustmentparameters can be reduced, so that the adjustment of the target sheetworkpiece is more precise.

In some embodiments, the second calibrating unit may calculate adifference between a current position and a standard positionof thetarget sheet workpiece, and a difference between a current size and astandard size of the target sheet workpiece as the deformation data ofthe target sheet workpiece.

In some embodiments, the second calibrating unit may determine thedeformation data through a first preset method based on the first targetimage. For more details about the deformation data, please refer to FIG.4 and its related descriptions.

In some embodiments, the second calibrating unit is configured to adjustthe clamping parameters through a second preset method based on the tiltdata and deformation data of the plurality of sheet workpieces. For moredetails, please refer to FIG. 6 and its related descriptions.

In step 350, sending, by an adjusting unit, the horizontal adjustmentdata, the rotational adjustment data, and the tilt adjustment data tothe production line through the sensor network platform to adjust abearing mechanism of the target sheet workpiece.

The bearing mechanism may be a mechanism that performs processing on thetarget sheet workpiece.

In some embodiments, the bearing mechanism may obtain the horizontaladjustment data, rotational adjustment data, and tilt adjustment data ofthe target sheet workpiece based on the sensor network platform, andadjust placement position and direction of the target sheet workpiecebefore processing.

In step 360, sending, by a communicating unit, the horizontal adjustmentdata, rotational adjustment data, and tilt adjustment data to the userplatform through the service platform for display.

In some embodiments, the communicating unit may send the horizontaladjustment data, rotational adjustment data, and tilt adjustment data ofthe target sheet workpiece to the user platform through the serviceplatform, and display these data to a user in sequence.

It should be noted that the above descriptions about the process 300 areonly for illustration and description, and do not limit the scope ofapplication of the present disclosure. For those skilled in the art,various modifications and changes can be made to the process 300 underthe guidance of the present disclosure. However, such modifications andchanges are still within the scope of the present disclosure.

FIG. 4 is a flowchart illustrating an exemplary process for determiningdeformation data according to some embodiments of the presentdisclosure.

As shown in FIG. 4 , process 400 includes step 410 and step 420.

In step 410, determining a relative position diagram of the target sheetworkpiece based on the first target image.

The relative position diagram of the target sheet work may be a diagramshowing an actual position relationship of a sheet workpiece. Forexample, the relative position diagram may be a feature diagramincluding nodes and edges. The nodes of the relative position diagrammay represent identification points or identification areas on the sheetworkpiece, adjacent nodes may be connected by edges, the edges of therelative position diagram may represent an adjacent relationship betweenthe identification points or identifiction areas. Attribute values ofthe nodes are coordinates of the identified points or identificationareas, and attribute values of the edges are a distance between theidentified points or identification areas. As shown in FIG. 5 b ,attribute value of node A in the relative position diagram 530 may beidentification area coordinate (1, 1), and attribute value of node Badjacent to node A may be identification area coordinate (1, 2),attribute value of an edge between node A and node B is 3.1, indicatingthat the distance between node A and node B is 3.1 mm.

In some embodiments, the relative position diagram of the target sheetworkpiece may be determined by extracting a position feature of thetarget sheet workpiece in the first target image.

In some embodiments, the relative position diagram of the target sheetworkpiece may be determined based on a relative position model. For moredetails about the relative position model, please refer to FIG. 5 a andits related descriptions.

In step 420, determining the deformation data of the target sheetworkpiece based on the relative position diagram of the target sheetworkpiece.

In some embodiments, the deformation data (including relative positionerror and size error) of the target sheet workpiece may be determinedbased on the relative position diagram of the target sheet workpiece.For more details about the deformation data, relative position error,and size error, please refer to related descriptions in the step 320 inFIG. 3 .

In some embodiments, the management platform may determine the relativeposition error by calculating a similarity between the relative positiondiagram and a standard relative position diagram.

The similarity is used to indicate a proximity degree of the relativeposition diagram to the standard relative position diagram. In someembodiments, the similarity between the relative position diagram andthe standard relative position diagram may be calculated by a histogram,hash value, Hamming distance, cosine distance, picture structure metric,or other feasible approaches.

In some embodiments, the management platform may determine the relativeposition error of the target sheet workpiece through a first presetmethod based on the similarity. The first preset method may includedimension difference calculation, coordinate difference calculation, orthe like. The first preset method may be determined based on equation(1):

$\begin{matrix}{\text{relative position error}\text{=}\text{1}\text{−}\text{similarity}} & \text{­­­(1)}\end{matrix}$

The first preset method may further include: according to a presetcorresponding relationship between the similarity and the relativeposition error, determining the relative position error corresponding tothe similarity (for example, by looking up a table based on thesimilarity). The first preset method may further include calculating therelative position error through a histogram, hash value, Hammingdistance, cosine distance, picture structure metric, or other feasibleapproaches.

In some embodiments, the management platform may determine the sizeerror of the target sheet workpiece based on the relative positiondiagram and the standard relative position diagram. In some embodiments,the management platform may calculate a sum of attribute values of alledges in the relative position diagram and a sum of attribute values ofall edges in the standard relative position based on the relativeposition diagram and the standard relative position diagram, determine achange rate (for example, the change rate may be a ratio of an absolutevalue after the subtraction of the two sums to the sum of attributevalues of all edges in the standard relative position diagram) based onthe sum of attribute values of all edges in the relative positiondiagram and the sum of attribute values of all edges in the standardrelative position diagram, and then determine the change rate as thesize error, or determine the size error corresponding to the change ratebased on a corresponding relationship between a preset change rate andpreset size error (for example, by looking up a table based on thechange rate).

In some embodiments of the present disclosure, the management platformdetermines the deformation data of the target sheet workpiece based onthe first target image. In this way, error of the deformation data canbe reduced, so that the obtained deformation data is more accurate, andthe subsequent adjustment of the target sheet workpiece is more precise.

FIG. 5 a is a schematic diagram illustrating an exemplary relativeposition model according to some embodiments of the present disclosure.

In some embodiments, the relative position model may be configured toobtain a relative position of each node of an image. There may be aplurality of types of the relative position model, for example,including a neural network model, a graph neural network model, or thelike.

In some embodiments, an input of relative position model 520 may includefirst target image 510; an output of the relative position model 520 mayinclude relative position diagram 530.

A graph structure of the relative position diagram 530 is shown in FIG.5 b , including a plurality of nodes A, B, C, D, E, F, G, H, and I, andedges connected between adjacent nodes, such as AB, BC, etc.

For more details about the first target image, please refer to FIG. 3and its related descriptions. For more details about the relativeposition diagram, please refer to FIG. 4 and its related descriptions.

In some embodiments, the relative position model 520 may be obtained bytraining a plurality of second training samples with a second label. Forexample, the plurality of second training samples with a second labelmay be input into an initial relative position model, a loss function isconstructed through the second label and a result of the initialrelative position model, and parameters of the initial relative positionmodel may be updated iteratively by gradient descent or other approachesbased on the loss function. When a preset condition is met, modeltraining is completed, and a trained relative position model isobtained. The preset condition may be that the loss function converges,a count of iterations reaches a threshold, or the like.

In some embodiments, the second training samples may at least includehistorical first target images. A label may be a correspondinghistorical relative position diagram, and the historical relativeposition diagram includes information such as nodes, edges, andcorresponding node values and edge values. The label may be obtainedbased on historical data, or be labeled manually.

In some embodiments of the present disclosure, the management platformobtains the relative position diagram based on the relative positionmodel. Obtaining the relative position diagram in this way makes data ofthe relative position diagram more accurate and improves the efficiencyof obtaining the relative position diagram.

FIG. 6 is a flowchart illustrating an exemplary process for adjustingclamping parameters according to some embodiments of the presentdisclosure. As shown in FIG. 6 , process 600 includes the followingcontents.

Clamping parameters of the target sheet workpiece may be parametersrelated to clamps acting on the workpiece before the workpiece starts tobe processed. For example, the clamping parameters of the target sheetworkpiece may include clamping force of a clamp, a placement position,or the like.

In some embodiments, the second calibrating unit may adjust the clampingparameters of the target sheet workpiece through a second preset methodbased on tilt data and deformation data of a plurality of target sheetworkpieces. The second preset method may include setting differentclamping parameters and repeatedly screening clamping parameters withthe smallest tilt data and deformation data, or other feasibleapproaches.

In step 610, processing the tilt data and deformation data of theplurality of target sheet workpieces to obtain feature value.

The feature value may be data representing the clamping force and theplacement position of the clamp of a bearing mechanism. For example, thefeature value may be determined from the tilt data (tilt direction, tiltangle), deformation data (size error, relative position error) of theplurality of target sheet workpieces, etc.

In some embodiments, the management platform may determine the featurevalue by extracting an average value of the tilt data and deformationdata or distribution parameters. The distribution parameters may bestandard deviation, variance, and other parameters reflectingdistribution.

In step 620, in response to the feature value not satisfying a presetcondition, determining candidate adjustment clamping parameters througha vector database.

In some embodiments, when the feature value does not satisfy a presetcondition, candidate adjustment clamping parameters may be determinedthrough a vector database. The preset condition may include that thefeature value is within a threshold range, and the threshold may be setby an expert.

The vector database may be a database for storing historical adjustmentdata. For example, a vector database may be used to store a plurality ofsets of historical feature values. Each set of historical feature valuesmay include historical feature value, corresponding feature vectorbefore adjustment, and adjustment clamping parameters, etc.

The candidate adjustment clamping parameters may be a plurality ofparameters for adjusting the clamping force and the placement positionof the clamp. For example, candidate adjustment clamping parameters mayinclude (40, 4, 5), then the candidate adjustment clamping parametersindicate that the clamping force of the clamp is 40N, the placementposition is (4, 5), etc. Coordinates may be determined based on acoordinate system established on a clamping plane.

In some embodiments, the management platform may construct featurevectors based on the feature value, select one or more vectors whosedistance from the feature vectors is smaller than a threshold in thevector database based on the feature vectors as reference vectors, anddetermine clamping parameters corresponding to the reference vectors ascandidate adjustment clamping parameters. The threshold may be set by anexpert.

In some embodiments, the feature vectors may include the feature value(average value of the tilt data and the deformation data, thedistribution parameters, etc.). For example, feature vector may be (a,b, c, d), and the feature vector indicates that a sheet workpiece isaveragely tilted upward by b° in direction a; the deviation of theaverage size error with respect to a standard size is c mm²; and thedistribution parameters is d. As another example, feature vector may be(10, 2, -3, 1, -2), the feature vector indicates that a sheet workpieceis averagely tilted upward by 2° in the counterclockwise direction of10°; the average size error is 3 mm² smaller than a standard size; andthe relative position is (1, -2) from the standard position. Positionvectors may be determined based on a standard horizontal coordinatesystem.

For more details about determining reference vectors based on vectordistance, please refer to step 630 and its related descriptions.

In some embodiments, a calibrating unit may determine target adjustmentparameters based on historical adjustment data. For example, thecalibrating unit may determine clamping parameters corresponding tovectors with the closest distance (highest similarity) to the featurevectors among the reference vectors as the target adjustment clampingparameters.

In step 630, adjusting, by a calibrating unit, the candidate adjustmentclamping parameters to obtain target adjustment clamping parameters.

The target adjustment clamping parameters may be parameters that areactually used to adjust the clamping force and placement position of aclamp.

For more details about the target adjustment clamping parameters, pleaserefer to the candidate adjustment clamping parameters and its relateddescriptions.

In some embodiments, the management platform may determine adjustmentcoefficients based on the vector distance between the feature vectorsand the reference vectors, and determine the target adjustment clampingparameters based on the adjustment coefficients.

In some embodiments, the adjustment coefficients may be used torepresent a level of correlation between the candidate adjustmentclamping parameters and the target adjustment clamping parameters, andthe larger the adjustment coefficients, the smaller the correlation. Theadjustment coefficients may be determined based on the vector distancebetween the feature vectors and the reference vectors and a presetrelationship between the adjustment coefficients. The presetrelationship may be that the vector distance is proportional to theadjustment coefficients, for example, the larger the vector distancebetween the feature vectors and the reference vectors, the larger theadjustment coefficients and the smaller the correlation. The presetrelationship may be linear or non-linear.

In some embodiments, the target adjustment clamping parameters may becomprehensively determined based on the adjustment coefficients and thecandidate adjustment clamping parameters. Exemplarily, the targetadjustment clamping parameters may be determined by the followingequation (2):

$\begin{matrix}\begin{matrix}{\text{target adjustment clamping parameter}\text{=}{\sum_{1}^{n}\text{adjustment coefficents}_{\text{i}}}} \\{\times \text{candidate adjustment clamping parameters}_{\text{i}}}\end{matrix} & \text{­­­(2)}\end{matrix}$

where adjustment coefficents_(i) denotes adjustment coefficientscorresponding to the i-th candidate adjustment clamping parameters,candidate adjustment clamping parameters_(i) denotes the i-th candidateadjustment clamping parameters, i=1~n, n denotes a total count ofcandidate adjustment clamping parameters.

In some embodiments of the present disclosure, the calibrating unitdetermines the target adjustment clamping parameters based on thecandidate adjustment clamping parameters and the adjustmentcoefficients, which can make the obtained target adjustment clampingparameters more accurate and more in line with an actual adjustmentsituation.

In some embodiments, when the collected feature value meets the presetcondition, the management platform may correspondingly store the featurevectors before performing the target adjustment clamping parameters andthe target adjustment clamping parameters in a historical vectordatabase, so as to increase a count of data in the vector database andimprove the accuracy of target adjustment parameters.

In some embodiments of the present disclosure, the management platformadjusts the clamping parameters through a preset method, which canreduce the deformation of the sheet workpiece, reduce the error causedby the subsequent processing of the sheet workpiece, and improve thequality of a product.

The basic concepts have been described above, obviously, for thoseskilled in the art, the above-detailed disclosure is only an embodimentand does not constitute a limitation to the present disclosure. Althoughnot expressly stated here, those skilled in the art may make variousmodifications, improvements, and corrections to the present disclosure.Such modifications, improvements, and corrections are suggested in thepresent disclosure, so such modifications, improvements, and correctionsstill belong to the spirit and scope of the exemplary embodiments of thepresent disclosure.

Meanwhile, the present disclosure uses specific words to describe theembodiments of the present disclosure. For example, “one embodiment”,“an embodiment”, and/or “some embodiments” refer to a certain feature,structure, or characteristic related to at least one embodiment of thepresent disclosure. Therefore, it should be emphasized and noted thattwo or more references to “an embodiment” or “one embodiment” or “analternative embodiment” in different places in the present disclosure donot necessarily refer to the same embodiment. In addition, certainfeatures, structures, or characteristics in one or more embodiments ofthe present disclosure may be properly combined.

In addition, unless explicitly stated in the claims, the order ofprocessing elements and sequences described in the present disclosure,the use of numbers and letters, or the use of other names are not usedto limit the sequence of processes and methods in the presentdisclosure. While the foregoing disclosure has discussed by way ofvarious examples some embodiments of the invention that are presentlybelieved to be useful, it should be understood that such detail is forillustrative purposes only and that the appended claims are not limitedto the disclosed embodiments, but rather, the claims are intended tocover all modifications and equivalent combinations that fall within thespirit and scope of the embodiments of the present disclosure. Forexample, although the implementation of various components describedabove may be embodied in a hardware device, it may also be implementedas a software only solution, e.g., an installation on an existing serveror mobile device.

In the same way, it should be noted that in order to simplify theexpression disclosed in the present disclosure and help theunderstanding of one or more embodiments of the present disclosure, inthe foregoing description of the embodiments of the present disclosure,sometimes multiple features are combined into one embodiment, drawings,or descriptions thereof. This method of disclosure does not, however,imply that the subject matter of the present disclosure requires morefeatures than are recited in the claims. Rather, claimed subject mattermay lie in less than all features of a single foregoing disclosedembodiment.

In some embodiments, numbers describing the number of components andattributes are used. It should be understood that such numbers used inthe description of the embodiments use the modifiers “about”,“approximately” or “substantially” in some examples. Unless otherwisestated, “about”, “approximately” or “substantially” indicates that avariation of 20% is allowed forthe stated figure. Accordingly, in someembodiments, numerical parameters used in the present disclosure andclaims are approximations that can vary depending on the desiredcharacteristics of individual embodiments. In some embodiments,numerical parameters should take into account the specified significantdigits and adopt the general digit reservation method. Although thenumerical ranges and parameters used in some embodiments of the presentdisclosure to confirm the breadth of the range are approximations, inspecific embodiments, such numerical values should be set as preciselyas practicable.

Each patent, patent application, patent application publication, andother material, such as article, book, specification, publication,document, etc., cited in the present disclosure is hereby incorporatedby reference in its entirety. Application history documents that areinconsistent with or conflict with the content of the present disclosureare excluded, and documents (currently or later appended to the presentdisclosure) that limit the broadest scope of the claims of the presentdisclosure are excluded. It should be noted that if there is anyinconsistency or conflict between the descriptions, definitions, and/orterms used in the accompanying materials of the present disclosure andthe contents ofthe present disclosure, the descriptions, definitions,and/or terms used in the present disclosureshall prevail.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other modifications are alsopossible within the scope of the present disclosure. Therefore, by wayof example and not limitation, alternative configurations of theembodiments of the present disclosure may be considered consistent withthe teachings of the present disclosure. Accordingly, the embodiments ofthe present disclosure are not limited to the embodiments explicitlyintroduced and described in the present disclosure.

What is claimed is:
 1. An industrial Internet of Things for sheetworkpiece processing, comprising: a detecting module and a processingmodule, wherein the detecting module is configured to detect a machiningprocess of a sheet workpiece to obtain detection information; and theprocessing module is configured to adjust a production device based onthe detection information.
 2. The Industrial Internet of Things forsheet workpiece processing according to claim 1, further comprising auser platform, a service platform, a management platform, a sensornetwork platform, and an object platform connected in sequence, thedetecting module executed by the object platform and the sensor networkplatform, the processing module executed by the service platform, themanagement platform, and the user platform, wherein the managementplatform includes: an obtaining unit configured to obtain a first targetimage on a production line through the sensor network platform; whereinthe first target image is an image of a target sheet workpiece under afixed focal length condition; a generating unit configured to performsharpness analysis on the first target image to generate a sharpnessmatrix, calculate a tilt direction and a tilt angle of the target sheetworkpiece as tilt data according to the sharpness matrix, and determinedeformation data of the target sheet workpiece through a first presetmethod based on the first target image; a correcting unit configured toinput the tilt data and the first target image into a tilt correctionmodel, and receive a second target image output by the tilt correctionmodel; wherein the second target image is an image of the target sheetworkpiece in a completely horizontal state; a calibrating unit includinga first calibrating unit and a second calibrating unit; wherein thefirst calibrating unit is configured to perform calibration andidentification on the second target image, generate horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, and calculate tilt adjustment data according to thetilt data; and the second calibrating unit is configured to adjustclamping parameters through a second preset method based on the tiltdata and the deformation data of a plurality of target sheet workpieces;an adjusting unit configured to send the horizontal adjustment data, therotational adjustment data, and the tilt adjustment data to theproduction line through the sensor network platform to adjust a bearingmechanism of the target sheet workpiece; and a communicating unitconfigured to send the horizontal adjustment data, the rotationaladjustment data, and the tilt adjustment data to the user platformthrough the service platform for display.
 3. The Industrial Internet ofThings for sheet workpiece processing according to claim 2, wherein thegenerating unit is further configured to: search for sharpness elementswith a sharpness lower than a standard value and with a same sharpnessin the sharpness matrix as same degree sharpness elements; and connectthe same degree sharpness elements into a straight line in the firsttarget image, and calculate the tilt direction according to the straightline.
 4. The Industrial Internet of Things for sheet workpieceprocessing according to claim 3, wherein the generating unit is furtherconfigured to: obtain a plurality of straight lines connected by aplurality of sets of same degree sharpness elements in the first targetimage, and calculate the tilt angle according to position relationshipsbetween the plurality of straight lines and the tilt direction.
 5. TheIndustrial Internet of Things for sheet workpiece processing accordingto claim 2, wherein the correcting unit is further configured to: wheninputting the tilt data and the first target image into the tiltcorrection model, determine a correction direction according to the tiltdirection, and determine a correction gradient according to the tiltangle through the tilt correction model; and perform stretchingprocessing corresponding to the correction gradient on the first targetimage to the second target image according to the correction directionthrough the tilt correction model.
 6. The Industrial Internet of Thingsfor sheet workpiece processing according to claim 2, wherein thecalibrating unit is further configured to: identify a first featurepoint and a second feature point in the second target image; wherein thefirst feature point corresponds to an identification point or anidentification area of the target sheet workpiece; and the secondfeature point corresponds to another identification point or anotheridentification area of the target sheet workpiece; establish aconnection line between the first feature point and the second featurepoint as a calibration connection line; calculate an angle between thecalibration connection line and a standard connection line in a standardtemplate as the rotational adjustment data; wherein the standardtemplate is an image of the target sheet workpiece in a standardprocessing position; and the standard connection line is a connectionbetween a first feature point and a second feature point in the standardtemplate; and calculate a difference between a midpoint of thecalibration connection line and a midpoint of the standard connectionline on a horizontal axis and a vertical axis in a standard horizontalcoordinate system as the horizontal adjustment data; wherein thestandard horizontal coordinate system is a Cartesian coordinate systemestablished on a horizontal plane in the standard template.
 7. TheIndustrial Internet of Things for sheet workpiece processing accordingto claim 2, wherein the deformation data includes size error andrelative position error of the target sheet workpiece; the generatingunit is further configured to: determine a relative position diagram ofthe target sheet workpiece based on the first target image; anddetermine the deformation data of the target sheet workpiece based onthe relative position diagram of the target sheet workpiece.
 8. TheIndustrial Internet of Things for sheet workpiece processing accordingto claim 2, wherein the second calibrating unit is further configuredto: process the tilt data and the deformation data of the plurality oftarget sheet workpieces to obtain feature value; in response to thefeature value not satisfying a preset condition, determine candidateadjustment clamping parameters through a vector database; and adjust thecandidate adjustment clamping parameters to obtain target adjustmentclamping parameters.
 9. The Industrial Internet of Things for sheetworkpiece processing according to claim 1, wherein a service platformincludes a general service platform and at least two servicesub-platforms, and different service sub-platforms are configured toreceive different types of data transmitted by a management platform;the general service platform summarizes data collected by all theservice sub-platforms and sends the data to a user platform for display;the management platform includes a plurality of mutually independentmanagement sub-platforms, and an obtaining unit, a generating unit, acorrecting unit, a calibrating unit, an adjusting unit, and acommunicating unit are configured in a same management sub-platform; andthe sensor network platform includes a general sensor network platformand at least two sensor network sub-platforms, the general sensornetwork platform receives all production data on a production line, andthe sensor network sub-platforms send different types of production datato the management platform respectively.
 10. A control method ofindustrial Internet of Things for sheet workpiece processing,comprising: detecting a machining process of a sheet workpiece to obtaindetection information; and adjusting a production device based on thedetection information.
 11. The control method according to claim 10,comprising: obtaining a first target image on a production line througha sensor network platform; wherein the first target image is an image ofa target sheet workpiece under a fixed focal length condition;performing sharpness analysis on the first target image to generate asharpness matrix, and calculating a tilt direction and a tilt angle ofthe target sheet workpiece as tilt data according to the sharpnessmatrix; determining deformation data of the target sheet workpiecethrough a first preset method based on the first target image; inputtingthe tilt data and the first target image into a tilt correction model,and receiving a second target image output by the tilt correction model;wherein the second target image is an image of the target sheetworkpiece in a completely horizontal state; performing calibration andidentification on the second target image, generating horizontaladjustment data and rotational adjustment data corresponding to thesecond target image, and calculating tilt adjustment data according tothe tilt data; adjusting clamping parameters through a second presetmethod based on the tilt data and the deformation data of a plurality oftarget sheet workpieces; sending the horizontal adjustment data, therotational adjustment data, and the tilt adjustment data to theproduction line through the sensor network platform to adjust a bearingmechanism of the target sheet workpiece; and sending the horizontaladjustment data, the rotational adjustment data, and the tilt adjustmentdata to a user platform through a service platform for display.
 12. Thecontrol method according to claim 11, wherein the calculating a tiltdirection and a tilt angle of the target sheet workpiece as tilt dataaccording to the sharpness matrix includes: searching for sharpnesselements with a sharpness lower than a standard value and with a samesharpness in the sharpness matrix as same degree sharpness elements; andconnecting the same degree sharpness elements into a straight line inthe first target image, and calculating the tilt direction according tothe straight line.
 13. The control method according to claim 12, whereinthe calculating a tilt direction and a tilt angle of the target sheetworkpiece as tilt data according to the sharpness matrix furtherincludes: obtaining a plurality of straight lines connected in the firsttarget image by a plurality of sets of same degree sharpness elements,and calculating the tilt angle according to position relationshipsbetween the plurality of straight lines and the tilt direction.
 14. Thecontrol method according to claim 11, wherein the inputting the tiltdata and the first target image into a tilt correction model, andreceiving a second target image output by the tilt correction modelincludes: when inputting the tilt data and the first target image intothe tilt correction model, determining, through the tilt correctionmodel, a correction direction according to the tilt direction, and acorrection gradient according to the tilt angle; and performing, throughthe tilt correction model, stretching processing corresponding to thecorrection gradient on the first target image to the second target imageaccording to the correction direction.
 15. The control method accordingto claim 11, wherein the performing calibration and identification onthe second target image, and the generating horizontal adjustment dataand rotational adjustment data corresponding to the second target imageincludes: identifying a first feature point and a second feature pointin the second target image; wherein the first feature point correspondsto an identification point or an identification area of the target sheetworkpiece; and the second feature point corresponds to anotheridentification point or another identification area of the target sheetworkpiece; establishing a connection line between the first featurepoint and the second feature point as a calibration connection line;calculating an angle between the calibration connection line and astandard connection line in a standard template as the rotationaladjustment data; wherein the standard template is an image of the targetsheet workpiece in a standard processing position; and the standardconnection line is a connection line between a first feature point and asecond feature point in the standard template; and calculating adifference between a midpoint of the calibration connection line and amidpoint of the standard connection line on a horizontal axis andvertical axis in a standard horizontal coordinate system as thehorizontal adjustment data; wherein the standard horizontal coordinatesystem is a Cartesian coordinate system established on a horizontalplane in the standard template.
 16. The control method according toclaim 11, wherein the deformation data includes size error and relativeposition error of the target sheet workpiece; and the determiningdeformation data of the target sheet workpiece through a first presetmethod based on the first target image includes: determining a relativeposition diagram of the target sheet workpiece based on the first targetimage; and determining the deformation data of the target sheetworkpiece based on the relative position diagram of the target sheetworkpiece.
 17. The control method according to claim 11, wherein theadjusting clamping parameters through a second preset method based ontilt data and deformation data of a plurality of target sheet workpiecesincludes: processing the tilt data and the deformation data of theplurality of target sheet workpieces to obtain feature value; inresponse to the feature value not satisfying a preset condition,determining candidate adjustment clamping parameters through a vectordatabase; and adjusting the candidate adjustment clamping parameters toobtain target adjustment clamping parameters.
 18. A non-transitorycomputer-readable storage medium storing computer instructions, whereina computer executes the control method of Industrial Internet of Thingsfor sheet workpiece processing according to claim 10 when reading thecomputer instructions in the storage medium.